Data cleaning, also known as data cleansing or scrubbing, is a critical process in data science that involves detecting and correcting inaccuracies or inconsistencies in datasets. This essential step ensures that the data used for analysis is accurate, complete, and reliable. Real-world data is often messy, containing errors such as duplicates, missing values, and formatting issues. By addressing these problems, data cleaning enhances data quality, enabling more accurate analytical insights and improving the performance of machine learning models. Ultimately, effective data cleaning is vital for making informed decisions based on trustworthy data.